ABSTRACT BACKGROUND Increased familial risks in multiple sclerosis (MS) range from 300-fold for monozygotic twins to 20-40-fold for biological first-degree relatives, suggesting a genetic influence. Yet if one identical twin has MS the other usually will not. One way of sorting out the contributions of genes and environment is to study half-sibs. METHODS In a Canadian population-based sample of 16 000 MS cases seen at 14 regional MS clinics one half-sib (or more) was reported by 939 index cases. By interview we elicited information on family structure and an illness in half-sibs and any full brothers or sisters. FINDINGS The age-adjusted MS rate in the 1839 half-sibs of these index cases was 1.32 percent compared with 3.46 percent for the 1395 full sibs of the same cases (p<0.001; likelihood ratio test). There were no significant differences in risk for maternal versus paternal half-sibs (1.42 percent vs 1.19 percent) or for those raised together versus those raised apart from the index case (1.17 percent vs 1.47 percent). INTERPRETATION Besides demonstrating the power and the feasibility of using half-sib studies to throw light on the aetiology of complex disorders, our findings show that a shared environment does not account for familial risk in MS and that maternal effects (such as intrauterine and perinatal factors, breastfeeding, and genomic imprinting) have no demonstrable effect on familial risk. Halving the number of potentially contributory genes (by comparing full-sib and half-sib rates) lowers the risk of MS by a factor of 2.62, an observation consistent with a polygenic hypothesis.

[Show abstract][Hide abstract]ABSTRACT:
Genome-wide association studies (GWAS) identify disease-associations for single-nucleotide-polymorphisms (SNPs) from scattered genomic-locations. However, SNPs frequently reside on several different SNP-haplotypes, only some of which may be disease-associated. This circumstance lowers the observed odds-ratio for disease-association.
Here we develop a method to identify the two SNP-haplotypes, which combine to produce each person's SNP-genotype over specified chromosomal segments. Two multiple sclerosis (MS)-associated genetic regions were modeled; DRB1 (a Class II molecule of the major histocompatibility complex) and MMEL1 (an endopeptidase that degrades both neuropeptides and β-amyloid). For each locus, we considered sets of eleven adjacent SNPs, surrounding the putative disease-associated gene and spanning ∼200 kb of DNA. The SNP-information was converted into an ordered-set of eleven-numbers (subject-vectors) based on whether a person had zero, one, or two copies of particular SNP-variant at each sequential SNP-location. SNP-strings were defined as those ordered-combinations of eleven-numbers (0 or 1), representing a haplotype, two of which combined to form the observed subject-vector. Subject-vectors were resolved using probabilistic methods. In both regions, only a small number of SNP-strings were present. We compared our method to the SHAPEIT-2 phasing-algorithm. When the SNP-information spanning 200 kb was used, SHAPEIT-2 was inaccurate. When the SHAPEIT-2 window was increased to 2,000 kb, the concordance between the two methods, in both of these eleven-SNP regions, was over 99%, suggesting that, in these regions, both methods were quite accurate. Nevertheless, correspondence was not uniformly high over the entire DNA-span but, rather, was characterized by alternating peaks and valleys of concordance. Moreover, in the valleys of poor-correspondence, SHAPEIT-2 was also inconsistent with itself, suggesting that the SNP-string method is more accurate across the entire region.
Accurate haplotype identification will enhance the detection of genetic-associations. The SNP-string method provides a simple means to accomplish this and can be extended to cover larger genomic regions, thereby improving a GWAS's power, even for those published previously.

[Show abstract][Hide abstract]ABSTRACT:
Gait is one of the most frequently impaired bodily functions in multiple sclerosis (MS). Determining abnormal parameters of gait in early MS could influence MS treatment and rehabilitation. The purpose of this study was to determine whether increased step-length variability could be detected in minimally disabled patients with MS or clinically isolated syndrome (CIS) using a sensored walkway gait analysis system. Nine participants with MS/CIS and nine age- and gender-matched controls were recruited for this study. MS/CIS participants underwent a neurologic examination, and all participants completed a screening interview. Each participant completed three walks at a self-selected pace and three walks at a brisk pace across the GAITRite walkway (MAP/CIR Inc, Havertown, PA). Mean values for step-length variability, step length, and velocity were calculated for each participant's self-selected and brisk trials. Independent t tests were used to compare MS/CIS participants with controls, and effect sizes were calculated. Step-length variability in the left leg at the self-selected pace was found to be greater in participants with MS/CIS than in controls, although no significant differences were found in velocity or step length. Step-length variability measurement shows promise in detecting subtle gait dysfunction. Larger, prospective studies exploring step-length variability may determine its clinical viability for detecting subtle gait dysfunction and could lead to improved prognostication of disability progression in MS.

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